347 citations · 803 across the 15 of their papers we have counts for
18 papers
The Pangaea Architecture: Fault-Tolerant Heterogeneous Topological Codes via a Quantum Bus
Sheir Yarkoni, Chen Scheim, Daniel Hakshuri +1
We introduce Pangaea, a fault-tolerant quantum architecture that uses a quantum bus to mediate logical operations between remote patches of two-dimensional topological codes. The b…
Scalable embedding of parity constraints in quantum annealing hardware
Michele Cattelan, Jemma Bennett, Sheir Yarkoni +1
One of the main bottlenecks in solving combinatorial optimization problems with quantum annealers is the qubit connectivity in the hardware. A possible solution for larger connecti…
Challenges and Opportunities in Quantum Optimization
Amira Abbas, Andris Ambainis, Brandon Augustino +43
Recent advances in quantum computers are demonstrating the ability to solve problems at a scale beyond brute force classical simulation. As such, a widespread interest in quantum a…
Parallel circuit implementation of variational quantum algorithms
Michele Cattelan, Sheir Yarkoni
We present a method to split quantum circuits of variational quantum algorithms (VQAs) to allow for parallel training and execution, that maximally exploits the limited number of q…
Quantum Computing Techniques for Multi-Knapsack Problems
Abhishek Awasthi, Francesco Bär, Joseph Doetsch +16
Optimization problems are ubiquitous in various industrial settings, and multi-knapsack optimization is one recurrent task faced daily by several industries. The advent of quantum…
Modeling routing problems in QUBO with application to ride-hailing
Michele Cattelan, Sheir Yarkoni
Many emerging commercial services are based on the sharing or pooling of resources for common use with the aim of reducing costs. Businesses such as delivery-, mobility-, or transp…
Quantum-Enhanced Selection Operators for Evolutionary Algorithms
David Von Dollen, Sheir Yarkoni, Daniel Weimer +2
Genetic algorithms have unique properties which are useful when applied to black box optimization. Using selection, crossover, and mutation operators, candidate solutions may be ob…
Solving workflow scheduling problems with QUBO modeling
A. I. Pakhomchik, S. Yudin, M. R. Perelshtein +2
In this paper we investigate the workflow scheduling problem, a known NP-hard class of scheduling problems. We derive problem instances from an industrial use case and compare agai…
Equivariant quantum circuits for learning on weighted graphs
Andrea Skolik, Michele Cattelan, Sheir Yarkoni +2
Variational quantum algorithms are the leading candidate for advantage on near-term quantum hardware. When training a parametrized quantum circuit in this setting to solve a specif…
Quantum Annealing for Industry Applications: Introduction and Review
Sheir Yarkoni, Elena Raponi, Thomas Bäck +1
Quantum annealing is a heuristic quantum optimization algorithm that can be used to solve combinatorial optimization problems. In recent years, advances in quantum technologies hav…
Multi-car paint shop optimization with quantum annealing
Sheir Yarkoni, Alex Alekseyenko, Michael Streif +3
We present a generalization of the binary paint shop problem (BPSP) to tackle an automotive industry application, the multi-car paint shop (MCPS) problem. The objective of the opti…
Beating classical heuristics for the binary paint shop problem with the quantum approximate optimization algorithm
Michael Streif, Sheir Yarkoni, Andrea Skolik +2
The binary paint shop problem (BPSP) is an APX-hard optimization problem of the automotive industry. In this work, we show how to use the Quantum Approximate Optimization Algorithm…
Quantum Shuttle: Traffic Navigation with Quantum Computing
Sheir Yarkoni, Florian Neukart, Eliane Moreno Gomez Tagle +5
The Web Summit conference in Lisbon, Portugal, is one of the biggest technology conferences in Europe, attended by tens of thousands of people every year. The high influx of people…
Semi-supervised time series classification method for quantum computing
Sheir Yarkoni, Andrii Kleshchonok, Yury Dzerin +2
In this paper we develop methods to solve two problems related to time series (TS) analysis using quantum computing: reconstruction and classification. We formulate the task of rec…
Traffic flow optimization using a quantum annealer
Florian Neukart, Gabriele Compostella, Christian Seidel +3
Quantum annealing algorithms belong to the class of meta-heuristic tools, applicable for solving binary optimization problems. Hardware implementations of quantum annealing, such a…
Quantum Annealing amid Local Ruggedness and Global Frustration
James King, Sheir Yarkoni, Jack Raymond +5
A recent Google study [Phys. Rev. X, 6:031015 (2016)] compared a D-Wave 2X quantum processing unit (QPU) to two classical Monte Carlo algorithms: simulated annealing (SA) and quant…
Global warming: Temperature estimation in annealers
Jack Raymond, Sheir Yarkoni, Evgeny Andriyash
Sampling from a Boltzmann distribution is NP-hard and so requires heuristic approaches. Quantum annealing is one promising candidate. The failure of annealing dynamics to equilibra…
Benchmarking a quantum annealing processor with the time-to-target metric
James King, Sheir Yarkoni, Mayssam M. Nevisi +2
In the evaluation of quantum annealers, metrics based on ground state success rates have two major drawbacks. First, evaluation requires computation time for both quantum and class…